{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors\n",
    "This notebook will start by covering what K-Nearest Neighbors (KNN) is, how it works, and how to use KNN in Python. Throughout this notebook we will also go over what pipelines are and how to use them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is K-Nearest Neighbors\n",
    "\n",
    "K-nearest neighbors is a model that uses the \"K\" most similar observations in order to make a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"images/KNN-Classification.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Video\n",
    "\n",
    "# Couldn't identify the source of this video. \n",
    "Video(\"images/KNN-Classification.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is roughly how K-Nearest Neighbors works:\n",
    "1. User specifies value for K. In this example above, we choose K=5 neighbors around black point.\n",
    "2. Search for the K observations in the data that are nearest to the measurements of an unknown sample\n",
    "    * Euclidian distance is often used as the distance metric\n",
    "3. Use the most popular target value from the K nearest neighbors as the predicted target value. In the example above, out of 5 nearest neighbors of black point, 2 are brown and 3 are green. Since we have a majority of green points around this black point we assign green label to it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Advantages of KNN</b>\n",
    "\n",
    "Easier to understand and explain than other machine learning algorithms\n",
    "\n",
    "Can be used for classification or regression\n",
    "\n",
    "<b>Disadvantages of KNN</b>\n",
    "\n",
    "It must store all of the training data. \n",
    "\n",
    "Its prediction phase can be slow when n is large\n",
    "\n",
    "Typically worse performance than other supervised learning methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# For scaling data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "The Iris dataset is one of datasets scikit-learn comes with that do not require the downloading of any file from some external website. The code below loads the iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_iris()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['target'] = data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrange Data into Features Matrix and Target Vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For demonstrational purposes, we are going take two features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, ['sepal length (cm)', 'sepal width (cm)']]\n",
    "#X = df.loc[:, df.columns != 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.loc[:, 'target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state = 0,\n",
    "                                                    test_size = .2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN in `scikit-learn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1:</b> Import the model you want to use\n",
    "\n",
    "In sklearn, all machine learning models are implemented as Python classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2:</b> Make an instance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier()\n"
     ]
    }
   ],
   "source": [
    "print(knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3:</b> Train the model on the data, storing the information learned from the data. Model is learning the relationship between features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4:</b> Predict the labels of new data\n",
    "\n",
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 2, 0, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 0, 2, 1, 0, 0, 1, 1,\n",
       "       0, 0, 2, 0, 0, 2, 1, 0])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate classification accuracy\n",
    "score = knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '3-Class classification (k = 5)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\n",
    "cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X_train.loc[:, 'sepal length (cm)'].values.min() - 1, X_train.loc[:, 'sepal length (cm)'].values.max() + 1\n",
    "y_min, y_max = X_train.loc[:, 'sepal width (cm)'].values.min() - 1, X_train.loc[:, 'sepal width (cm)'].values.max() + 1\n",
    "\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light, shading='nearest')\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X_train.loc[:, 'sepal length (cm)'].values,\n",
    "            X_train.loc[:, 'sepal width (cm)'].values,\n",
    "            c=y_train,\n",
    "            cmap=cmap_bold,\n",
    "            edgecolor='k',\n",
    "            s=20)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"3-Class classification (k = 5)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Data (YouTube Thumbnail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the color maps\n",
    "cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\n",
    "cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])\n",
    "h = .01  # step size in the mesh\n",
    "\n",
    "# Create figure and axes with specified size and white background\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 9), facecolor='white')\n",
    "\n",
    "# Determine the min and max values for x and y\n",
    "x_min, x_max = X_train.loc[:, 'sepal length (cm)'].values.min() - 1, X_train.loc[:, 'sepal length (cm)'].values.max() + 1\n",
    "y_min, y_max = X_train.loc[:, 'sepal width (cm)'].values.min() - 1, X_train.loc[:, 'sepal width (cm)'].values.max() + 1\n",
    "\n",
    "# Create a mesh grid\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "# Predict class using the mesh grid\n",
    "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "ax.pcolormesh(xx, yy, Z, cmap=cmap_light, shading='nearest')\n",
    "\n",
    "# Plot also the training points\n",
    "ax.scatter(X_train.loc[:, 'sepal length (cm)'].values,\n",
    "           X_train.loc[:, 'sepal width (cm)'].values,\n",
    "           c=y_train,\n",
    "           cmap=cmap_bold,\n",
    "           edgecolor='k',\n",
    "           s=130)\n",
    "\n",
    "# Set the limits of the plot\n",
    "ax.set_xlim(xx.min(), xx.max())\n",
    "ax.set_ylim(yy.min(), yy.max())\n",
    "\n",
    "ax.tick_params(labelsize = 18)\n",
    "\n",
    "ax.set_xlabel('sepal length (cm)', fontsize = 30)\n",
    "ax.set_ylabel('sepal width (cm)', fontsize = 30)\n",
    "ax.set_title(\"Decision Boundaries on Iris using KNN (k=5)\", fontsize = 48)\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig('KNN_3_Class_Classification.png', dpi = 950)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning k\n",
    "When k is low, KNN is considered a low bias, high variance model. \n",
    "\n",
    "When k is high, KNN is considered a high bias, low variance model. \n",
    "\n",
    "In the video, as K is increased, the classification spaces' borders become more distinct. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"images/KNNlowtoHigh.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source not clear for this video\n",
    "# Maybe machinelearningknowledge?\n",
    "Video(\"images/KNNlowtoHigh.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor num_neighbors in range(1, 51):\\n\\n    # Make an instance of the Model\\n    knn = KNeighborsClassifier(n_neighbors=num_neighbors)\\n\\n    # Train the model on the data\\n    knn.fit(X_train, y_train)\\n\\n    cmap_light = ListedColormap([\\'orange\\', \\'cyan\\', \\'cornflowerblue\\'])\\n    cmap_bold = ListedColormap([\\'darkorange\\', \\'c\\', \\'darkblue\\'])\\n    h = .005  # step size in the mesh\\n\\n\\n    # Plot the decision boundary. For that, we will assign a color to each\\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\\n    x_min, x_max = X_train.loc[:, \\'sepal length (cm)\\'].values.min() - 1, X_train.loc[:, \\'sepal length (cm)\\'].values.max() + 1\\n    y_min, y_max = X_train.loc[:, \\'sepal width (cm)\\'].values.min() - 1, X_train.loc[:, \\'sepal width (cm)\\'].values.max() + 1\\n\\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\\n                         np.arange(y_min, y_max, h))\\n\\n    \\n    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\\n\\n    # Put the result into a color plot\\n    Z = Z.reshape(xx.shape)\\n    plt.figure(figsize = (7,7))\\n    plt.pcolormesh(xx, yy, Z, cmap=cmap_light )\\n\\n    # Plot also the training points\\n    plt.scatter(X_train.loc[:, \\'sepal length (cm)\\'].values,\\n                X_train.loc[:, \\'sepal width (cm)\\'].values,\\n                c=y_train,\\n                cmap=cmap_bold,\\n                edgecolor=\\'k\\',\\n                s=40)\\n    plt.xlim(xx.min(), xx.max())\\n    plt.ylim(yy.min(), yy.max())\\n    plt.xticks(fontsize = 15)\\n    plt.yticks(fontsize = 15)\\n    plt.title(\"3-Class classification k = \" + str(num_neighbors), fontsize = 15)\\n    plt.savefig(\\'imagesanimation/\\' + \\'initial\\' + str(num_neighbors).zfill(4) + \\'.png\\', dpi = 5000)\\n    plt.cla()\\n'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code that generated the images for the video\n",
    "\n",
    "\"\"\"\n",
    "for num_neighbors in range(1, 51):\n",
    "\n",
    "    # Make an instance of the Model\n",
    "    knn = KNeighborsClassifier(n_neighbors=num_neighbors)\n",
    "\n",
    "    # Train the model on the data\n",
    "    knn.fit(X_train, y_train)\n",
    "\n",
    "    cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\n",
    "    cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])\n",
    "    h = .005  # step size in the mesh\n",
    "\n",
    "\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "    x_min, x_max = X_train.loc[:, 'sepal length (cm)'].values.min() - 1, X_train.loc[:, 'sepal length (cm)'].values.max() + 1\n",
    "    y_min, y_max = X_train.loc[:, 'sepal width (cm)'].values.min() - 1, X_train.loc[:, 'sepal width (cm)'].values.max() + 1\n",
    "\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "\n",
    "    \n",
    "    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure(figsize = (7,7))\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light )\n",
    "\n",
    "    # Plot also the training points\n",
    "    plt.scatter(X_train.loc[:, 'sepal length (cm)'].values,\n",
    "                X_train.loc[:, 'sepal width (cm)'].values,\n",
    "                c=y_train,\n",
    "                cmap=cmap_bold,\n",
    "                edgecolor='k',\n",
    "                s=40)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.xticks(fontsize = 15)\n",
    "    plt.yticks(fontsize = 15)\n",
    "    plt.title(\"3-Class classification k = \" + str(num_neighbors), fontsize = 15)\n",
    "    plt.savefig('imagesanimation/' + 'initial' + str(num_neighbors).zfill(4) + '.png', dpi = 5000)\n",
    "    plt.cla()\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore. this turns the images into a video\n",
    "#!ffmpeg -framerate 1 -i 'initial%04d.png' -c:v libx264 -r 30 -pix_fmt yuv420p initial_002.mp4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benefits of Pipelines\n",
    "Pipelines are a simply way to keep your data processing and modeling code organized. Specifically a pipeline bundles preprocessing and modeling steps so you can use the whole bundle as if it were a single step.\n",
    "\n",
    "* Cleaner Code: You don’t need to keep track of your training data at each step of processing. Accounting for data at each step of processing can get messy. \n",
    "* Fewer Bugs: There are fewer opportunities to mis-apply a step or forget a pre-processing step\n",
    "* More options for model testing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrange Data into Features Matrix and Target Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns != 'target']\n",
    "y = df.loc[:, 'target'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state = 0,\n",
    "                                                    test_size = .2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN in `scikit-learn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce dimension to 2 with PCA\n",
    "std_clf = make_pipeline(StandardScaler(),\n",
    "                        PCA(n_components=2, random_state=0),\n",
    "                        KNeighborsClassifier(n_neighbors=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_clf.fit(X_train, y_train)\n",
    "pred_test_std = std_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prediction accuracy for the standardized test dataset with PCA\n",
      "93.33%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\nPrediction accuracy for the standardized test dataset with PCA')\n",
    "print('{:.2%}\\n'.format(metrics.accuracy_score(y_test, pred_test_std)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract PCA from pipeline\n",
    "pca_std = std_clf.named_steps['pca']\n",
    "\n",
    "# Use PCA with scale on X_train data for visualization.\n",
    "scaler = std_clf.named_steps['standardscaler']\n",
    "X_train_std_transformed = pca_std.transform(scaler.transform(X_train))\n",
    "\n",
    "# visualize standardized  with PCA performed\n",
    "for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):\n",
    "    plt.scatter(X_train_std_transformed[y_train == l, 0],\n",
    "                X_train_std_transformed[y_train == l, 1],\n",
    "                color=c,\n",
    "                label='class %s' % l,\n",
    "                alpha=0.5,\n",
    "                marker=m\n",
    "                )\n",
    "\n",
    "plt.title('Standardized training dataset after PCA')\n",
    "plt.xlabel('1st principal component')\n",
    "plt.ylabel('2nd principal component')\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid()\n",
    "\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
